Statistics of Emergency Accidents in the Refining And petrochemical Industry for the 2007-2016 Years

Krasnov, A. N.; Sadykova, Z.Kh.; Perezhogin, D.Yu.; Мухин, И. А.

doi:10.17122/ogbus-2017-6-179-191

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…В настоящее время в России, как указано в Единой межведомственной информационно-статистической системе (ЕМИСС) 1 , насчитывается 8139 резервуаров для хранения нефтепродуктов общей вместимостью 16 512,17 тыс. м 3 и 772 резервуара для хранения нефти общей вместимостью 5590,58 тыс.…”

Section: аннотацияunclassified

“…Рост объемов добычи нефти и активное развитие нефтедобывающей и нефтеперерабатывающей промышленности в России увеличивают количество 1 ЕМИСС. URL: https://www.fedstat.ru/organizations/ 2 Итоги работы Минэнерго России и основные результаты функционирования ТЭК в 2018 году.…”

Section: аннотацияunclassified

“…URL: https://minenergo.gov.ru/node/18288 пожаров, способствуя росту их масштабов и наносимому ими ущербу. За период с 2007 по 2016 гг., как указано в [1], произошло 126 чрезвычайных ситуаций. Динамика возникновения различных типов чрезвычайных ситуаций (пожар, взрыв или выброс) представлена на рис.…”

Section: аннотацияunclassified

“…An increase in oil production and intensive development of oil production and oil refi ning industries in Russia boost the number of fi res, thus, leading to the growth of the fi re scale and the damage caused. From 2007 to 2016, as indicated in [1], 126 emergencies occurred. The dynamics of various emergency situations (fi re, explosion or emission) is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, in Russia, as stated by the Unifi ed Interdepartmental Statistical Information System (EMISS) 1 , there are 8,139 tanks for oil products with a total capacity of 16,512,170 m 3 and 772 tanks for crude oil with a total capacity of 5,590,580 m 3 . According to the fi nal reports of the Russian Ministry of Energy for 2018 2 and 2019 3 , 54 fi elds were commissioned in the Russian oil industry in 2018, 31 fi elds in 2019; and in 2018, the total length of new oil pipelines reached 216 km, in 2019 -324 km.…”

Section: Introductionmentioning

confidence: 99%

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Prompt forecasting of heat ﬂows under ﬁre conditions in a vertical steel tank having an ANFIS protective wall

Stankevich

Balner

Trčka

et al. 2020

Požarovzryvobezopasnostʹ

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Introduction. Tanks and tank farms are widespread in many constituent entities of the Russian Federation and are among the most important elements of the model for the production, treatment, transportation and processing of oil and oil products. It is relevant both at the Russian and global levels to ensure that ﬁre safety is arranged for tank farms to reduce highest risk levels according to the risk-based safety model. In the context of information and communication technology (ICT) developments and introduction of ICT into the operation and management of various facilities, over the past decades advanced methods have emerged for predicting the occurrence and development of emergency situations at facilities and enhancing management decisions on containment and elimination of emergency situations including ﬁres.Goals and objectives. In this paper, the authors present a model that they developed to promptly forecast heat ﬂows using artiﬁcial neural networks. The forecast model will improve the safety of ﬁre brigade personnel responsible for extinguishing ﬁres inside ground-based vertical steel tanks having protective walls. In the research, the authors place special emphasis on identifying dependence between the heat ﬂow and the wind load. Methods. To achieve this goal, the authors arranged and conducted a series of experiments, collected experimental data on heat ﬂows, and created training and test samples.Results. Dependences between heat ﬂows and environmental factors were identiﬁed by constructing adaptive neuro-fuzzy inference systems or adaptive network-based fuzzy inference systems (ANFIS). Various types of membership functions, optimisation and system generation methods were compared and it was found out that for ANFISs, prediction of heat ﬂows with regard to and disregarding wind loads were optimal, if subcluster and hybrid optimisation methods were used, as they had lowest error values for samples.Discussion. The analysis shows that wind speed and tank location can rise temperatures of the air, tank wall and petrol. Therefore, despite the complexity of the analysis, the regard for all these factors makes it possible to identify a safe distance between a burning tank and ﬁreﬁghters.Conclusions. The research made it possible to develop a model for prompt heat ﬂow forecasting with the help of artiﬁcial intelligence elements (ANFIS). The results obtained in the course of the work make it possible to increase the efﬁciency of prompt forecasting of the dynamic behaviour of ﬁre inside tanks and tank farms and optimize managerial decision-making by responsible persons.

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Section: аннотацияunclassified